Table_2_Automated segmentation of colorectal liver metastasis and liver ablation on contrast-enhanced CT images.docx

ObjectivesColorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones.
MethodsFour FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from the liver tumor segmentation (LiTS) challenge. For the evaluation, we acquired 15 patients from the 3D-IRCADb database, 18 patients from our institution (CRLM = 24, ablation-zone = 19), and those submitted to the LiTS challenge (n = 70). Qualitative evaluations of our institutional data were performed by two board-certified radiologists (interventional and diagnostic) and a radiology-trained physician fellow, using a Likert scale of 1–5.
ResultsThe most accurate model was the Hybrid-WNet. On a patient-by-patient basis in the 3D-IRCADb dataset, the median (min–max) Dice similarity coefficient (DSC) was 0.73 (0.41–0.88), the median surface distance was 1.75 mm (0.57–7.63 mm), and the number of false positives was 1 (0–4). In the LiTS challenge (n = 70), the global DSC was 0.810. The model sensitivity was 98% (47/48) for sites ≥15 mm in diameter. Qualitatively, 100% (24/24; minority vote) of the CRLM and 84% (16/19; majority vote) of the ablation zones had Likert scores ≥4.
ConclusionThe Hybrid-WNet model provided fast (<30 s) and accurate segmentations of CRLM and ablation zones on contrast-enhanced CT scans, with positive physician reviews.

研究目标
结直肠癌（Colorectal cancer, CRC）是美国第三大常见癌症，同时也是全球范围内癌症相关死亡的主要诱因之一。多达60%的患者会出现肝转移（CRLM）。放疗、消融治疗等临床治疗手段需先进行病灶分割，用于制定治疗计划并实施治疗。针对消融治疗而言，还需对消融区域进行分割以评估病灶覆盖效果。本研究假设，采用新型训练方法的全卷积（fully convolutional, FC）神经网络，可快速且精准地完成CRLM与消融区域的识别及分割。

研究方法
本研究考察了四种全卷积神经网络模型架构：标准3D-UNet、残差3D-UNet、密集3D-UNet与Hybrid-WNet。模型以肝脏肿瘤分割（Liver Tumor Segmentation, LiTS）挑战赛的92例患者数据进行训练。评估所用数据集涵盖三部分：来自3D-IRCADb数据库的15例患者、本机构的18例患者（其中CRLM病例24例、消融区域病例19例），以及提交至LiTS挑战赛的70例患者数据。针对本机构的患者数据，由两名执业认证放射科医师（分别从事介入放射与诊断放射方向）与一名经过放射学培训的医师进修生，采用1-5李克特量表开展定性评估。

研究结果
综合性能最优的模型为Hybrid-WNet。在3D-IRCADb数据集的逐患者评估中，戴斯相似性系数（Dice Similarity Coefficient, DSC）的中位数（最小值至最大值）为0.73（0.41~0.88），中位表面距离为1.75mm（0.57~7.63mm），假阳性数量为1（0~4）。在LiTS挑战赛数据集（n=70）中，全局戴斯相似性系数为0.810。对于直径≥15mm的病灶，该模型的敏感度为98%（47/48）。定性评估结果显示，100%（24/24；少数投票结果）的CRLM病例与84%（16/19；多数投票结果）的消融区域病例的李克特评分≥4分。

研究结论
Hybrid-WNet模型可在增强CT扫描中实现快速（<30秒）且精准的CRLM与消融区域分割，且获得了参与评估医师的正面认可。